def test_vqe_caching_direct(self, batch_mode=True):
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = self.algo_input.qubit_op.num_qubits
init_state = Zero(num_qubits)
var_form = RY(num_qubits, 3, initial_state=init_state)
optimizer = L_BFGS_B()
algo = VQE(self.algo_input.qubit_op,
var_form,
optimizer,
'matrix',
batch_mode=batch_mode)
quantum_instance_caching = QuantumInstance(backend,
circuit_caching=True,
skip_qobj_deepcopy=True,
skip_qobj_validation=True)
result_caching = algo.run(quantum_instance_caching)
self.assertLessEqual(quantum_instance_caching.circuit_cache.misses, 0)
self.assertAlmostEqual(
self.reference_vqe_result['statevector_simulator']['energy'],
result_caching['energy'])
speedup_check = 3
self.log.info(
result_caching['eval_time'],
self.reference_vqe_result['statevector_simulator']['eval_time'] /
speedup_check)
def test_saving_and_loading(self):
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = self.algo_input.qubit_op.num_qubits
init_state = Zero(num_qubits)
var_form = RY(num_qubits, 3, initial_state=init_state)
optimizer = L_BFGS_B()
algo = VQE(self.algo_input.qubit_op, var_form, optimizer, 'matrix')
fd, cache_tmp_file = tempfile.mkstemp(suffix='.inp')
os.close(fd)
quantum_instance_caching = QuantumInstance(backend,
circuit_caching=True,
cache_file=cache_tmp_file,
skip_qobj_deepcopy=True,
skip_qobj_validation=True)
algo.run(quantum_instance_caching)
self.assertLessEqual(quantum_instance_caching.circuit_cache.misses, 0)
is_file_exist = os.path.exists(cache_tmp_file)
self.assertTrue(is_file_exist, "Does not store content successfully.")
circuit_cache_new = CircuitCache(skip_qobj_deepcopy=True,
cache_file=cache_tmp_file)
self.assertEqual(quantum_instance_caching.circuit_cache.mappings,
circuit_cache_new.mappings)
self.assertLessEqual(circuit_cache_new.misses, 0)
if is_file_exist:
os.remove(cache_tmp_file)
def test_vqe_caching_direct(self, max_evals_grouped):
self._build_refrence_result(backends=['statevector_simulator'])
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = self.algo_input.qubit_op.num_qubits
init_state = Zero(num_qubits)
var_form = RY(num_qubits, 3, initial_state=init_state)
optimizer = L_BFGS_B()
algo = VQE(self.algo_input.qubit_op,
var_form,
optimizer,
max_evals_grouped=max_evals_grouped)
quantum_instance_caching = QuantumInstance(
backend,
circuit_caching=True,
skip_qobj_deepcopy=True,
skip_qobj_validation=True,
optimization_level=self.optimization_level)
result_caching = algo.run(quantum_instance_caching)
self.assertLessEqual(quantum_instance_caching.circuit_cache.misses, 0)
self.assertAlmostEqual(
self.reference_vqe_result['statevector_simulator']['energy'],
result_caching['energy'])
speedup_min = 3
speedup = result_caching['eval_time'] / self.reference_vqe_result[
'statevector_simulator']['eval_time']
self.assertLess(speedup, speedup_min)
def test_tapered_op(self):
""" tapered op test """
tapered_ops = self.z2_symmetries.taper(self.qubit_op)
smallest_idx = 0 # Prior knowledge of which tapered_op has ground state
the_tapered_op = tapered_ops[smallest_idx]
optimizer = SLSQP(maxiter=1000)
init_state = HartreeFock(num_qubits=the_tapered_op.num_qubits,
num_orbitals=self.core._molecule_info['num_orbitals'],
qubit_mapping=self.core._qubit_mapping,
two_qubit_reduction=self.core._two_qubit_reduction,
num_particles=self.core._molecule_info['num_particles'],
sq_list=the_tapered_op.z2_symmetries.sq_list)
var_form = UCCSD(num_qubits=the_tapered_op.num_qubits, depth=1,
num_orbitals=self.core._molecule_info['num_orbitals'],
num_particles=self.core._molecule_info['num_particles'],
active_occupied=None, active_unoccupied=None,
initial_state=init_state,
qubit_mapping=self.core._qubit_mapping,
two_qubit_reduction=self.core._two_qubit_reduction,
num_time_slices=1,
z2_symmetries=the_tapered_op.z2_symmetries)
algo = VQE(the_tapered_op, var_form, optimizer)
backend = BasicAer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend=backend)
algo_result = algo.run(quantum_instance)
_, result = self.core.process_algorithm_result(algo_result)
self.assertAlmostEqual(result['energy'], self.reference_energy, places=6)
def test_saving_and_loading_e2e(self):
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = self.algo_input.qubit_op.num_qubits
init_state = Zero(num_qubits)
var_form = RY(num_qubits, 1, initial_state=init_state)
optimizer = L_BFGS_B(maxiter=10)
algo = VQE(self.algo_input.qubit_op, var_form, optimizer)
with tempfile.NamedTemporaryFile(suffix='.inp',
delete=True) as cache_tmp_file:
cache_tmp_file_name = cache_tmp_file.name
quantum_instance_caching = QuantumInstance(
backend,
circuit_caching=True,
cache_file=cache_tmp_file_name,
skip_qobj_deepcopy=True,
skip_qobj_validation=True,
optimization_level=self.optimization_level)
algo.run(quantum_instance_caching)
self.assertLessEqual(quantum_instance_caching.circuit_cache.misses,
0)
is_file_exist = os.path.exists(cache_tmp_file_name)
self.assertTrue(is_file_exist,
"Does not store content successfully.")
circuit_cache_new = CircuitCache(skip_qobj_deepcopy=True,
cache_file=cache_tmp_file_name)
self.assertEqual(quantum_instance_caching.circuit_cache.mappings,
circuit_cache_new.mappings)
self.assertLessEqual(circuit_cache_new.misses, 0)
def test_end2end_h2(self, name, optimizer, backend, shots):
""" end to end h2 """
del name # unused
if optimizer == 'COBYLA':
optimizer = COBYLA()
optimizer.set_options(maxiter=1000)
elif optimizer == 'SPSA':
optimizer = SPSA(max_trials=2000)
ryrz = RYRZ(self.qubit_op.num_qubits, depth=3, entanglement='full')
vqe = VQE(self.qubit_op, ryrz, optimizer, aux_operators=self.aux_ops)
quantum_instance = QuantumInstance(backend, shots=shots)
results = vqe.run(quantum_instance)
self.assertAlmostEqual(results['energy'], self.reference_energy, places=4)
def test_end2end_h2(self, name, optimizer, backend, mode, shots):
if optimizer == 'COBYLA':
optimizer = COBYLA()
optimizer.set_options(maxiter=1000)
elif optimizer == 'SPSA':
optimizer = SPSA(max_trials=2000)
ryrz = RYRZ(self.algo_input.qubit_op.num_qubits, depth=3, entanglement='full')
vqe = VQE(self.algo_input.qubit_op, ryrz, optimizer, mode, aux_operators=self.algo_input.aux_ops)
run_config = RunConfig(shots=shots, max_credits=10, memory=False)
quantum_instance = QuantumInstance(backend, run_config)
results = vqe.run(quantum_instance)
self.assertAlmostEqual(results['energy'], self.reference_energy, places=4)
def test_tapered_op(self):
# set_qiskit_chemistry_logging(logging.DEBUG)
tapered_ops = []
for coeff in itertools.product([1, -1], repeat=len(self.sq_list)):
tapered_op = Operator.qubit_tapering(self.qubit_op, self.cliffords,
self.sq_list, list(coeff))
tapered_ops.append((list(coeff), tapered_op))
smallest_idx = 0 # Prior knowledge of which tapered_op has ground state
the_tapered_op = tapered_ops[smallest_idx][1]
the_coeff = tapered_ops[smallest_idx][0]
optimizer = SLSQP(maxiter=1000)
init_state = HartreeFock(
num_qubits=the_tapered_op.num_qubits,
num_orbitals=self.core._molecule_info['num_orbitals'],
qubit_mapping=self.core._qubit_mapping,
two_qubit_reduction=self.core._two_qubit_reduction,
num_particles=self.core._molecule_info['num_particles'],
sq_list=self.sq_list)
var_form = UCCSD(
num_qubits=the_tapered_op.num_qubits,
depth=1,
num_orbitals=self.core._molecule_info['num_orbitals'],
num_particles=self.core._molecule_info['num_particles'],
active_occupied=None,
active_unoccupied=None,
initial_state=init_state,
qubit_mapping=self.core._qubit_mapping,
two_qubit_reduction=self.core._two_qubit_reduction,
num_time_slices=1,
cliffords=self.cliffords,
sq_list=self.sq_list,
tapering_values=the_coeff,
symmetries=self.symmetries)
algo = VQE(the_tapered_op, var_form, optimizer, 'matrix')
backend = BasicAer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend=backend)
algo_result = algo.run(quantum_instance)
lines, result = self.core.process_algorithm_result(algo_result)
self.assertAlmostEqual(result['energy'],
self.reference_energy,
places=6)
def test_vqe_caching_direct(self, batch_mode):
backend = get_aer_backend('statevector_simulator')
num_qubits = self.algo_input.qubit_op.num_qubits
init_state = Zero(num_qubits)
var_form = RY(num_qubits, 3, initial_state=init_state)
optimizer = L_BFGS_B()
algo = VQE(self.algo_input.qubit_op,
var_form,
optimizer,
'matrix',
batch_mode=batch_mode)
circuit_cache = CircuitCache(skip_qobj_deepcopy=True)
quantum_instance_caching = QuantumInstance(backend,
circuit_cache=circuit_cache,
skip_qobj_validation=True)
result_caching = algo.run(quantum_instance_caching)
self.assertLessEqual(circuit_cache.misses, 1)
self.assertAlmostEqual(
self.reference_vqe_result['statevector_simulator']['energy'],
result_caching['energy'])
def run_VQE(self):
"""Runs the Variational Quantum Eigensolver (VQE)"""
qubit_op = self.construct_wpo_operator()
self.ref = self.exact_eigensolver(qubit_op)
# Setting initial state, variational form, and backend
init_state = self.init_state_name(self.num_qubits)
var_form = self.vf_name(self.num_qubits,
depth=self.depth,
entanglement='linear',
initial_state=init_state)
# Don't use SPSA if using a noiseless simulator
if self.device == 'statevector_simulator':
optimizers = [COBYLA, L_BFGS_B, SLSQP]
else:
optimizers = [COBYLA, SPSA]
print(self.param_table())
# Initializing empty lists & dicts for storage
df = pd.DataFrame()
algos = {}
algo_results = {}
for optimizer in optimizers:
# For reproducibility
aqua_globals.random_seed = 250
print(f'\rOptimizer: {optimizer.__name__} ', end='')
counts = []
values = []
params = []
def store_intermediate_result(eval_count, parameters, mean, std):
counts.append(eval_count)
values.append(mean)
params.append(parameters)
# Running VQE
algo = VQE(qubit_op,
var_form,
optimizer(),
callback=store_intermediate_result)
quantum_instance = QuantumInstance(backend=self.backend,
shots=self.shots)
algo_result = algo.run(quantum_instance)
df[optimizer.__name__] = pd.Series(data=values, index=counts)
algos[optimizer.__name__] = algo
algo_results[optimizer.__name__] = algo_result
print('\rOptimization complete')
self.algos = algos
self.result_df = df
self.algo_results = algo_results
self.optimizers = algo_results.keys()
self.results_to_save = {
'file_path': self.file_path,
'ref': self.ref,
'n_qubits': self.num_qubits,
'n_paulis': self.num_paulis,
'depth': self.depth,
'n_shots': self.shots,
'algo_results': self.algo_results,
'result_df': self.result_df
}
for i in range(len(optimizers)):
aqua_globals.random_seed = 250
optimizer = optimizers[i]()
print('\rOptimizer: {} '.format(type(optimizer).__name__), end='')
counts = []
values = []
params = []
def store_intermediate_result(eval_count, parameters, mean, std):
counts.append(eval_count)
values.append(mean)
params.append(parameters)
algo = VQE(qubit_op,
var_form,
optimizer,
callback=store_intermediate_result)
quantum_instance = QuantumInstance(backend=backend)
algo_result = algo.run(quantum_instance)
converge_cnts[i] = np.asarray(counts)
converge_vals[i] = np.asarray(values)
param_vals[i] = np.asarray(params)
print('\rOptimization complete ')
# In[12]:
ee = ExactEigensolver(qubit_op)
result = ee.run()
ref = result['energy']
print('Reference value: {}'.format(ref))
def run_ucc_r12(opt):
"""
Parameters
----------
* opt dictionary with user and default options/thresholds
Returns
-------
* UCC-R12 ground state energy
"""
import itertools
import logging
import numpy as np
import time
from datetime import datetime
import qiskit
from qiskit import BasicAer,Aer
from qiskit.aqua import set_qiskit_aqua_logging, QuantumInstance
from qiskit.aqua.operators import Z2Symmetries, WeightedPauliOperator
from qiskit.aqua.algorithms.adaptive import VQE
from qiskit.aqua.algorithms.classical import ExactEigensolver
from qiskit.aqua.components.optimizers import L_BFGS_B,CG,SPSA,SLSQP, COBYLA
from qiskit.chemistry.drivers import PySCFDriver, UnitsType, HFMethodType
from qiskit.chemistry.core import TransformationType, QubitMappingType
from qiskit.chemistry.aqua_extensions.components.initial_states import HartreeFock
from qiskit.chemistry import set_qiskit_chemistry_logging
# ----- local functions ----- #
import sys
sys.path.append('/Users/mario/Documents/GitHub/R12-F12/project_ec/ucc_ec/')
from qmolecule_ec import QMolecule_ec,build_parameter_list
from hamiltonian_ec import Hamiltonian
from uccsd_ec import UCCSD
from davidson import davidson
#import logging
#set_qiskit_chemistry_logging(logging.DEBUG)
logfile = open(opt["logfile"],'w')
dateTimeObj = datetime.now()
logfile.write("date and time "+str(dateTimeObj)+"\n")
logfile.write("\n\n")
import subprocess
label = subprocess.check_output(['git','log','-1','--format="%H"']).strip()
logfile.write("git commit "+str(label)+"\n")
logfile.write("\n\n")
qiskit_dict = qiskit.__qiskit_version__
logfile.write("qiskit version \n")
for k in qiskit_dict:
logfile.write(k+" : "+qiskit_dict[k]+"\n")
logfile.write("\n\n")
logfile.write("local run options \n")
for k in opt:
logfile.write(k+" : "+str(opt[k])+"\n")
logfile.write("\n\n")
import time
t0 = time.time()
if(opt["input_file"] is None):
# ----- normal molecular calc ----- #
if(opt["calc_type"].lower() == "rhf"): hf=HFMethodType.RHF
if(opt["calc_type"].lower() == "rohf"): hf=HFMethodType.ROHF
if(opt["calc_type"].lower() == "uhf"): hf=HFMethodType.UHF
driver = PySCFDriver(atom=opt["geometry"],unit=UnitsType.ANGSTROM,charge=opt["charge"],spin=opt["spin"],basis=opt["basis"],hf_method=hf)
molecule = driver.run()
molecule_ec = QMolecule_ec(molecule=molecule,filename=None,logfile=logfile)
else:
# ----- custom matrix elements ----- #
molecule_ec = QMolecule_ec(molecule=None,filename=opt["input_file"],calc=opt["calc_type"],nfreeze=opt["nfreeze"],logfile=logfile)
molecule_ec.mo_eri_ints_ec = (molecule_ec.mo_eri_ints_ec).transpose((0,1,3,2))
t1 = time.time()
logfile.write("classical ES and setup: %f [s] \n" % (t1-t0))
logfile.write("\n\n")
core = Hamiltonian(qubit_mapping=QubitMappingType.PARITY,two_qubit_reduction=True,freeze_core=False)
qubit_op, _ = core.run(molecule_ec)
t2 = time.time()
logfile.write("second-quantized Hamiltonian setup : %f [s] \n" % (t2-t1))
logfile.write("\n\n")
logfile.write("Original number of qubits %d \n" % (qubit_op.num_qubits))
z2_symmetries = Z2Symmetries.find_Z2_symmetries(qubit_op)
nsym = len(z2_symmetries.sq_paulis)
the_tapered_op = qubit_op
sqlist = None
z2syms = None
if(nsym>0):
logfile.write('\nZ2 symmetries found: \n')
for symm in z2_symmetries.symmetries:
logfile.write(symm.to_label())
logfile.write('\nsingle-qubit operators found: \n')
for sq in z2_symmetries.sq_paulis:
logfile.write(sq.to_label())
logfile.write('\nCliffords found: \n')
for clifford in z2_symmetries.cliffords:
logfile.write(clifford.print_details())
logfile.write('\nsingle-qubit list: {} \n'.format(z2_symmetries.sq_list))
tapered_ops = z2_symmetries.taper(qubit_op)
for tapered_op in tapered_ops:
logfile.write("Number of qubits of tapered qubit operator: %d \n" % (tapered_op.num_qubits))
t3 = time.time()
logfile.write("detection of symmetries: %f [s] \n" % (t3-t2))
smallest_eig_value = 99999999999999
smallest_idx = -1
for idx in range(len(tapered_ops)):
td0 = time.time()
from utils import retrieve_SCF_energy
print("In sector ",idx,len(tapered_ops))
curr_value = retrieve_SCF_energy(tapered_ops[idx].copy(),core,opt) #,parm_list)
curr_value = np.abs(curr_value-molecule_ec.hf_energy)
print("Deviation ",curr_value)
if curr_value < smallest_eig_value:
smallest_eig_value = curr_value
smallest_idx = idx
if(curr_value<1e-6): break
td1 = time.time()
val = curr_value
logfile.write("Lowest-energy computational basis state of the {}-th tapered operator is %s %f \n" % (str(idx),val))
logfile.write("HF search time %f: \n" % (td1-td0))
the_tapered_op = tapered_ops[smallest_idx]
the_coeff = tapered_ops[smallest_idx].z2_symmetries.tapering_values
logfile.write("{}-th tapered operator, with corresponding symmetry sector of {}".format(smallest_idx, the_coeff))
logfile.write("\nNumber of qubits in the {}-th tapered operator {}\n\n".format(smallest_idx,the_tapered_op.num_qubits))
sqlist = the_tapered_op.z2_symmetries.sq_list
z2syms = the_tapered_op.z2_symmetries
# ========
# setup initial state
# ========
init_state = HartreeFock(num_qubits=the_tapered_op.num_qubits, num_orbitals=core._molecule_info['num_orbitals'],
qubit_mapping=core._qubit_mapping, two_qubit_reduction=core._two_qubit_reduction,
num_particles=core._molecule_info['num_particles'],sq_list=sqlist)
# ---- initial parameter guess
init_parm = None
if(opt["start_pt"].lower()=="file"):
init_parm = np.loadtxt('vqe.parameters')
if(opt["var_form"].lower()=="uccsd" and opt["start_pt"].lower()=="ccsd"):
parm_list = build_parameter_list(molecule_ec)
logfile.write("Initial parameters = %d\n" % len(parm_list))
for mu,p in enumerate(parm_list):
logfile.write('%d %f\n' % (mu,p))
if(opt["var_form"].lower()=="uccsd"):
var_form = UCCSD(num_qubits=the_tapered_op.num_qubits,depth=opt["UCCSD_depth"],
num_orbitals=core._molecule_info['num_orbitals'],
num_particles=core._molecule_info['num_particles'],
active_occupied=opt["UCCSD_active_occupied"], active_unoccupied=opt["UCCSD_active_unoccupied"], initial_state=init_state,
qubit_mapping=core._qubit_mapping, two_qubit_reduction=core._two_qubit_reduction,
num_time_slices=opt["UCCSD_num_time_slices"],z2_symmetries=z2syms,init_parm=parm_list)
if(opt["var_form"].lower()=="uccsd" and opt["start_pt"].lower()=="ccsd"):
nparm,ndepth = len(var_form._mask),var_form._depth
init_parm = np.zeros(nparm*ndepth)
for idp in range(ndepth):
for ims in range(nparm):
init_parm[ims+idp*nparm] = parm_list[var_form._mask[ims]]
logfile.write("Selected parameters = %d\n" % nparm)
for mu,p in enumerate(var_form._mask):
logfile.write('%d %f\n' % (p,parm_list[p]))
elif(opt["var_form"].lower()=="ry"):
var_form = RY(the_tapered_op.num_qubits,depth=opt["R_depth"],
entanglement=opt["R_entanglement"],initial_state=HF_state)
elif(opt["var_form"].lower()=="ryrz"):
var_form = RYRZ(the_tapered_op.num_qubits,depth=opt["R_depth"],
entanglement=opt["R_entanglement"],initial_state=HF_state)
elif(opt["var_form"].lower()=="swaprz"):
var_form = SwapRZ(the_tapered_op.num_qubits,depth=opt["R_depth"],
entanglement=opt["R_entanglement"],initial_state=HF_state)
else:
print("invalid variational form")
assert(False)
# setup optimizer
if( opt["optimizer"].lower()=="bfgs"): optimizer = L_BFGS_B(maxiter=opt["max_eval"])
elif(opt["optimizer"].lower()=="cg"): optimizer = CG(maxiter=opt["max_eval"])
elif(opt["optimizer"].lower()=="slsqp"): optimizer = SLSQP(maxiter=opt["max_eval"])
elif(opt["optimizer"].lower()=="spsa"): optimizer = SPSA()
elif(opt["optimizer"].lower()=="cobyla"): optimizer = COBYLA(maxiter=opt["max_eval"])
else: print("not coded yet"); assert(False)
# set vqe
if(opt["var_form"].lower()=="uccsd"): algo = VQE(the_tapered_op,var_form,optimizer,initial_point=init_parm)
else: algo = VQE(the_tapered_op,var_form,optimizer)
# setup backend
backend = Aer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend=backend)
t0 = time.time()
algo_result = algo.run(quantum_instance)
t1 = time.time()
logfile.write("\nVQE time [s] %f \n\n" % (t1-t0))
result = core.process_algorithm_result(algo_result)
for line in result[0]:
logfile.write(line+"\n")
logfile.write("\nThe parameters for UCCSD are:\n")
for i,(tc,tq) in enumerate(zip(init_parm,algo_result['opt_params'])):
logfile.write("%d %f %f \n" % (i,tc,tq))
if(opt["print_parameters"]):
par_file = open('vqe.parameters','w')
for p in algo_result['opt_params']:
par_file.write("%f \n" % p)
par_file.close()
#td0 = time.time()
#ee = davidson(the_tapered_op,'fci')
#td1 = time.time()
#logfile.write("\n\nExact diagonalization, energy: %f \n" % (ee+molecule_ec.nuclear_repulsion_energy+molecule_ec.energy_offset_ec))
#logfile.write("Davidson FCI time: %f [s] \n" % (td1-td0))
print('============================================================================')
print(' DONE!')
print('============================================================================')
return 0
var_form = UCCSD(num_qubits=the_tapered_op.num_qubits,
depth=1,
num_orbitals=core._molecule_info['num_orbitals'],
num_particles=core._molecule_info['num_particles'],
active_occupied=None,
active_unoccupied=None,
initial_state=init_state,
qubit_mapping=core._qubit_mapping,
two_qubit_reduction=core._two_qubit_reduction,
num_time_slices=1,
z2_symmetries=the_tapered_op.z2_symmetries,
shallow_circuit_concat=False)
# force_no_tap_excitation=True,
# method_doubles='succ',
# excitation_type='d',
# same_spin_doubles=False)
# set up VQE
algo = VQE(the_tapered_op, var_form, optimizer)
# Choose the backend (use Aer instead of BasicAer)
backend = Aer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend=backend, optimization_level=1)
# run the algorithm
algo_result = algo.run(quantum_instance)
# get the results
_, result = core.process_algorithm_result(algo_result)
print(result)
